Biology subjects

Piekos, S. N.

Publications and source records attributed to Piekos, S. N..

4 recordsLinked to original sources

bioRxiv2024

Placental Network Differences Among Obstetric Syndromes Identified With An Integrated Multiomics Approach

The placenta is essential for a healthy pregnancy, and placental pathology can endanger both maternal and fetal health. Placental function is affected by dynamic, complex, and interconnected molecular, cellular, and environmental events; therefore, we need a systems biology approach to study disease in normal physiological placenta function. We use placental multiomics (short and bulk transcriptomics, untargeted metabolomics, and targeted proteomics) paired with clinical data and placental histopathology reports from 321 placentas across multiple obstetric conditions: fetal growth restriction (FGR), FGR with pregnancy-related hypertension (FGR+HDP), preeclampsia (PE), and spontaneous preterm delivery (PTD). We first performed cellular deconvolution to estimate cell type numbers from bulk transcriptomes: FGR+HDP placentas were the most different from control placentas driven by a higher estimated number of extravillous trophoblast (p<0.0001). Next, we evaluated the impact of fetal sex and gestational age on analyte levels, adjusting for these confounders. We then generated obstetric condition-specific correlation networks and identified communities of related analytes associated with physiology and disease. We demonstrated how network connectivity and its disruption in disease can be used to identify signatures unique to a clinical outcome. We examined a community defined in control placentas for which the most connected node was miR-365a-3p in contrast to the corresponding community in FGR+HDP placentas for which the most connected node was hypoxia-induced miR-210-3p. From this community, we identified a signature containing mRNA transcripts implicated in placental dysfunction (e.g. FLT1, FSTL3, HTRA4, LEP, and PHYHIP). This signature distinguishes between FGR+HDP placentas and placentas of differing clinical outcomes in high-dimensional space. These findings illustrate the power of systems biology-driven interomics network analysis in a single tissue type, laying the groundwork for future multi-tissue studies.

systems biology↗

bioRxiv2020

Biomedical Graph Visualizer for Identifying Drug Candidates

1.Millions of Americans suffer from illnesses with non-existent or ineffective drug treatment. Identifying plausible drug candidates is a major barrier to drug development due to the large amount of time and resources required; approval can take years when people are suffering now. While computational tools can expedite drug candidate discovery, these tools typically require programming expertise that many biologists lack. Though biomedical databases continue to grow, they have proven difficult to integrate and maintain, and non-programming interfaces for these data sources are scarce and limited in capability. This creates an opportunity for us to present a suite of user-friendly software tools to aid computational discovery of novel treatments through de novo discovery or repurposing. Our tools eliminate the need for researchers to acquire computational expertise by integrating multiple databases and offering an intuitive graphical interface for analyzing these publicly available data. We built a computational knowledge graph focused on biomedical concepts related to drug discovery, designed visualization tools that allow users to explore complex relationships among entities in the graph, and served these tools through a free and user-friendly web interface. We show that users can conduct complex analyses with relative ease and that our knowledge graph and algorithms recover approved repurposed drugs. Our evaluation indicates that our method provides an intuitive, easy, and effective toolkit for discovering drug candidates. We show that our toolkit makes computational analysis for drug development more accessible and efficient and ultimately plays a role in bringing effective treatments to all patients. Our application is hosted at: https://biomedical-graph-visualizer.wl.r.appspot.com/

bioinformatics↗

bioRxiv2018

Feedback Regulation between Initiation and Maturation Networks Orchestrates the Chromatin Dynamics of Epidermal Lineage Commitment

Tissue development results from lineage-specific transcription factors (TF) programming a dynamic chromatin landscape through progressive cell fate transitions. Here, we interrogate the epigenomic landscape during epidermal differentiation and create an inference network that ranks the coordinate effects of TF-accessible regulatory element-target gene expression triplets on lineage commitment. We discover two critical transition periods: surface ectoderm initiation and keratinocyte maturation, and identify TFAP2C and p63 as lineage initiation and maturation factors, respectively. Surprisingly, we find that TFAP2C, and not p63, is sufficient to initiate surface ectoderm differentiation, with TFAP2C-initiated progenitor cells capable of maturing into functional keratinocytes. Mechanistically, TFAP2C primes the surface ectoderm chromatin landscape and induces p63 expression and binding sites, thus allowing maturation factor p63 to positively auto-regulate its expression and close a subset of the TFAP2C-initiated early program. Our work provides a general framework to infer TF networks controlling chromatin transitions that will facilitate future regenerative medicine advances.

cell biology↗

bioRxiv2018

Morphogen-Lineage Selector Interactions During Surface Epithelial Commitment

Human embryonic stem cell (hESC) differentiation promises advances in regenerative medicine1-3, yet conversion of hESCs into tissues such as keratinocytes requires a better understanding of epigenetic interactions between the inductive morphogens retinoic acid (RA) and bone morphogenetic protein 4 (BMP), and the master regulator p634,5. Here we develop a robust, defined, keratinocyte differentiation system, and use a multi-dimensional genomics approach to interrogate the contributions of the morphogens and lineage selector to chromatin dynamics during early surface ectoderm commitment. In stark contrast to other master regulators6-9, we find using p63 gain and loss of function hESC lines, that p63 effects major transcriptional changes only after morphogenetic action. Morphogens alter chromatin accessibility and histone modifications, establishing an epigenetic landscape for p63 to modify. In turn, p63 closes chromatin accessibility and promotes the accumulation of repressive H3K27me3 histone modifications at sites distal to where it binds. Surprisingly, cohesin HiChIP10 visualization of genome-wide chromosome conformation reveals that both p63 and the morphogens contribute to dynamic long-range genomic interactions that increase the probability of negative transcriptional regulation at p63 target loci. p63-regulated accessibility, not H3K27me3 deposition, appears to drive early transcriptional changes. We illustrate morphogen-selector interactions by studying p63 negative feedback regulation of TFAP2Ci11, whereby disruption of the single p63 binding site results in a loss of p63-mediated transcriptional control and dramatic increases in TFAP2C and p63 expression. Our study reveals the unexpected dependency of p63 on morphogenetic signaling to control long-range chromatin interactions during tissue specification and provides novel insights into how master regulators specify diverse morphological outcomes.

genetics↗